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Knowing what You Know: valid and validated confidence sets in multiclass and multilabel prediction
TLDR
To address the potential challenge of exponentially large confidence sets in multilabel prediction, this work builds tree-structured classifiers that efficiently account for interactions between labels that can be bolted on top of any classification model to guarantee its validity.
Robust Validation: Confident Predictions Even When Distributions Shift
TLDR
A method is presented that produces prediction sets (almost exactly) giving the right coverage level for any test distribution in an $f$-divergence ball around the training population, and achieves (nearly) valid coverage in finite samples.
Knowing what you know: valid confidence sets in multiclass and multilabel prediction
TLDR
To address the potential challenge of exponentially large confidence sets in multilabel prediction, this work builds tree-structured classifiers that efficiently account for interactions between labels that can be bolted on top of any classification model to guarantee its validity.
The $s$-value: evaluating stability with respect to distributional shifts
Common statistical measures of uncertainty like p-values and confidence intervals quantify the uncertainty due to sampling, that is, the uncertainty due to not observing the full population. In
Predictive Inference with Weak Supervision
TLDR
A methodology to bridge the gap between partial supervision and validation is presented, developing a conformal prediction framework to provide valid predictive confidence sets—sets that cover a true label with a prescribed probability, independent of the underlying distribution—using weakly labeled data.
Noise Reduction in Subsonic Jets Using Chevron Nozzles
The exhaust of the jet engines used in commercial (subsonic) aircrafts produce a lot of noise known as jet noise. This noise has adverse effects on the population living nearby the airports. However,